Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)
| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 29/7/2025 | Comida | 10000 | Andrés | complemento comida |
| 29/7/2025 | Electrodomésticos/mantención casa | 68000 | Andrés | NA |
| 30/7/2025 | Comida | 24140 | Tami | Supermercado |
| 31/7/2025 | Gas | 19100 | Andrés | NA |
| 3/8/2025 | Comida | 86089 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot)
plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2)
tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F)
print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0105e+09 2 5.213 0.0056 **
## lag_depvar 2.6334e+11 1 2717.032 <2e-16 ***
## Residuals 8.1804e+10 844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1788.215 16245.89 0.1444422
## 2-0 31301.983 23190.400 39413.57 0.0000000
## 2-1 24073.145 19380.923 28765.37 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
## 41 30103.29 1 31422.29
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## 848 51313.14 2 51727.43
## 849 56125.29 2 51313.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 692 53536.24 21975.822
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2016.90228 4039.52184 -537.19306 2438.83341 -2967.89396 519.37768
## 8 9 10 11 12 13
## -5654.91965 -1188.74614 -3967.17058 -420.00697 -4941.45163 -1612.37140
## 14 15 16 17 18 19
## -902.67835 374.83043 -3244.83896 -380.48099 -2132.26169 6601.75793
## 20 21 22 23 24 25
## -1529.04741 -1208.51388 1475.18425 -1186.33894 234.65442 1695.26787
## 26 27 28 29 30 31
## -7101.06143 946.36963 8191.98204 420.89054 -11.19124 -2397.82968
## 32 33 34 35 36 37
## 1578.11878 4575.00912 1130.94354 2395.67138 -1863.17696 4611.85775
## 38 39 40 41 42 43
## 4306.07549 -2271.91329 -2978.82024 -1108.79961 -10740.60914 7286.57435
## 44 45 46 47 48 49
## 2557.21579 1367.66367 8106.34953 689.88394 6532.28537 6719.66653
## 50 51 52 53 54 55
## -5875.47099 -4791.32012 -5057.65370 -7928.36748 6127.42592 -4076.67418
## 56 57 58 59 60 61
## -4895.70084 3853.01159 886.64515 -32.86513 141.56210 -4996.96075
## 62 63 64 65 66 67
## 18124.69603 3644.78411 -3641.26283 5928.48751 7348.81862 14645.60746
## 68 69 70 71 72 73
## 1704.05450 -13202.49456 -1301.28836 4647.48839 -4895.17685 -4401.49662
## 74 75 76 77 78 79
## -10496.02493 2465.09876 -5400.27613 1061.88335 -6867.39997 544.04775
## 80 81 82 83 84 85
## -2356.74702 -2696.29564 -3934.62447 -540.90235 2311.65368 3760.85428
## 86 87 88 89 90 91
## 476.41426 -484.85237 196.18954 4301.61786 -1162.04563 1151.36593
## 92 93 94 95 96 97
## -2063.67050 -1044.17020 177.41490 274.71157 -7483.99866 2389.77179
## 98 99 100 101 102 103
## -8603.45184 -2943.58188 -4042.60951 -1740.25618 -1264.57433 3178.06296
## 104 105 106 107 108 109
## -2343.57057 2592.17640 -1159.23223 969.50443 2586.58640 -3154.08367
## 110 111 112 113 114 115
## -4723.56991 -851.88040 1901.99273 11692.81067 -1240.03771 2670.48175
## 116 117 118 119 120 121
## 4265.35249 3506.13650 -1095.84749 -4712.92493 -3722.26301 2320.50511
## 122 123 124 125 126 127
## -1731.24794 1341.31062 8859.50868 850.73252 133.97152 -2518.03267
## 128 129 130 131 132 133
## 2657.32638 7055.33477 1016.93321 -8495.09579 1750.73953 4137.41662
## 134 135 136 137 138 139
## -3161.08510 -1418.00427 -852.76552 -3879.08746 1182.89986 -495.18969
## 140 141 142 143 144 145
## -2913.39790 1717.64611 -1880.99072 -7829.65681 2037.11941 -3481.14966
## 146 147 148 149 150 151
## 2100.07649 -258.78227 1021.82077 -360.08049 1351.42163 1186.32387
## 152 153 154 155 156 157
## 3356.63273 -4860.83005 -1174.89962 -3236.45849 5955.32980 9747.04702
## 158 159 160 161 162 163
## -3652.37155 -4999.10354 3382.70451 -24.93821 2476.83740 -6129.21324
## 164 165 166 167 168 169
## -6967.32565 3935.72400 17172.27905 3403.06002 -624.40454 -2672.28145
## 170 171 172 173 174 175
## -1330.53946 3363.86227 -455.74315 -8303.31019 2636.39166 4097.29284
## 176 177 178 179 180 181
## 396.25502 8520.63721 -9480.17969 -3702.16139 -10974.81276 -11469.01223
## 182 183 184 185 186 187
## 1007.28291 9064.88732 -1660.86394 5697.38346 6321.30630 12920.14814
## 188 189 190 191 192 193
## 8185.05559 -4315.43201 2211.03539 10111.18158 -1908.10858 -2709.88538
## 194 195 196 197 198 199
## -10545.75939 -6623.92536 976.10524 -5490.98561 -10050.45322 5134.97019
## 200 201 202 203 204 205
## -3318.63598 -1960.54062 -1050.95202 6248.01180 9630.00775 317.18211
## 206 207 208 209 210 211
## 2661.98881 2832.68821 5516.82531 12562.38685 -5966.71954 -11570.17263
## 212 213 214 215 216 217
## -5931.18092 -10846.94662 -5326.25954 1280.16841 -13257.49683 16151.87278
## 218 219 220 221 222 223
## 7559.40578 1271.37542 26429.65750 12244.96827 7043.44469 13730.30796
## 224 225 226 227 228 229
## -4219.41102 -2040.87163 3481.85575 65.04670 2455.06511 8716.10375
## 230 231 232 233 234 235
## 5541.02113 -2193.04550 -2108.62009 9150.07495 -11787.87944 -7552.29678
## 236 237 238 239 240 241
## -8803.45260 -10356.89544 2829.71745 1102.29106 -8547.48752 -9233.62616
## 242 243 244 245 246 247
## 8857.08374 -8010.61707 2247.50961 -10543.72193 -4289.38886 1188.97409
## 248 249 250 251 252 253
## 766.43246 -12555.04072 3412.49122 1826.28220 3971.61092 1888.79683
## 254 255 256 257 258 259
## -1410.24647 10888.86589 20617.83895 2909.75111 -4562.73798 3822.56686
## 260 261 262 263 264 265
## -1985.33811 3449.12046 -5142.66587 -11178.21766 -5000.92116 -788.75164
## 266 267 268 269 270 271
## -5454.86434 8516.50987 -4553.49439 3920.10555 -2382.43241 4156.90546
## 272 273 274 275 276 277
## 428.59140 7020.87410 -1704.41595 11734.09425 -4892.84416 1422.50960
## 278 279 280 281 282 283
## -677.36991 7547.48330 -5371.98199 -3036.18660 -11559.91991 -2947.99650
## 284 285 286 287 288 289
## 18381.32960 7480.31480 2419.18870 -946.39171 591.18334 6083.77609
## 290 291 292 293 294 295
## 6559.13627 -19104.63802 -11432.47198 -8390.67920 9413.15863 2803.23747
## 296 297 298 299 300 301
## -1452.73045 27131.42479 9742.03081 4561.17343 9173.54438 2499.14551
## 302 303 304 305 306 307
## -1388.50396 7549.59861 -24651.08082 -3830.97395 -458.82271 -7247.18177
## 308 309 310 311 312 313
## -4232.03623 2683.26076 -9445.65375 -3461.01525 -8408.73220 1361.79769
## 314 315 316 317 318 319
## -3359.74116 1844.88437 -4293.71855 27240.20100 -1016.41609 3000.90579
## 320 321 322 323 324 325
## 10533.09598 5268.13148 32050.60395 4712.74327 -21332.98053 1459.79694
## 326 327 328 329 330 331
## 781.47503 -6789.05078 -2035.34908 -33558.82355 713.00724 -2471.28370
## 332 333 334 335 336 337
## -255.08147 -3329.50786 3931.34973 -604.18206 -7120.03689 -3267.05168
## 338 339 340 341 342 343
## -2336.49563 -7821.72939 3726.44710 -1512.92733 -1880.45636 -1136.32601
## 344 345 346 347 348 349
## 32.32300 332.16077 -1774.06590 -9602.30428 -13344.57847 2205.62104
## 350 351 352 353 354 355
## -4442.76263 -3773.08566 -6091.72038 1647.92058 1266.86553 2622.38312
## 356 357 358 359 360 361
## -3914.26550 -660.59182 527.14916 6854.45384 89.65591 -230.06253
## 362 363 364 365 366 367
## 2387.99035 -2955.98122 -1075.31361 -8939.29977 -4793.64825 -6366.90834
## 368 369 370 371 372 373
## -5086.80924 -7378.39590 4906.59240 236.82857 6976.91829 -7809.46713
## 374 375 376 377 378 379
## -2414.07072 -3535.67748 -2607.86545 -12594.93562 1800.94574 -10751.22701
## 380 381 382 383 384 385
## 5606.12479 9215.78198 2964.45237 -2577.50776 1429.09327 6558.00869
## 386 387 388 389 390 391
## 11196.52361 -6060.22908 -5604.19859 -383.05777 8336.09387 1555.75189
## 392 393 394 395 396 397
## 10955.95206 -10184.04696 2503.87593 433.43527 282.46456 -933.54488
## 398 399 400 401 402 403
## -838.17464 -14758.03967 8311.79965 -1418.07085 -1601.92771 6759.83660
## 404 405 406 407 408 409
## -8178.55854 -1509.65109 -2736.37909 -6011.63502 -3027.46434 -4074.60615
## 410 411 412 413 414 415
## -8899.33905 6019.06647 1496.28489 -7529.05178 -7824.22911 14112.23833
## 416 417 418 419 420 421
## 3642.01144 4295.35972 -8255.06088 -4933.20520 -2772.21767 2657.52357
## 422 423 424 425 426 427
## -14186.52616 -2915.59556 -9217.22880 2923.14951 6866.57062 6428.92775
## 428 429 430 431 432 433
## -4166.10741 -4287.25255 -4875.92425 -1929.08691 -5849.41750 -6747.92059
## 434 435 436 437 438 439
## -6052.70786 -1482.44123 -941.40210 -5075.08267 2491.03133 4728.82670
## 440 441 442 443 444 445
## -5194.61126 -2283.51054 1452.21011 -3974.24205 2706.85148 -6723.98577
## 446 447 448 449 450 451
## -12236.98416 -4600.02329 9563.81050 -2157.81741 4630.07646 -6016.89934
## 452 453 454 455 456 457
## -1252.69067 252.78928 2888.83158 -12421.28261 3256.01960 -6832.76689
## 458 459 460 461 462 463
## 6409.04863 2869.59656 2349.50680 -4015.09321 1935.12989 -174.97558
## 464 465 466 467 468 469
## 1623.48780 -698.72173 3173.68473 -2828.69404 5625.64149 -7141.62742
## 470 471 472 473 474 475
## -3136.44420 -2365.90196 -4816.45443 2859.25275 7647.05941 -6196.40942
## 476 477 478 479 480 481
## 1326.59041 -6342.38909 -2986.85308 1877.31807 -13074.24676 -9858.98492
## 482 483 484 485 486 487
## -1280.42028 -62.63876 -1053.64667 -1437.93068 -9683.80897 11020.08515
## 488 489 490 491 492 493
## 6114.19525 7273.16365 -5611.52880 5209.94874 9115.81358 5845.29147
## 494 495 496 497 498 499
## -13699.89834 -10742.40298 -3581.09805 -1236.03918 -653.53223 -7756.27528
## 500 501 502 503 504 505
## 503.37848 4173.54922 5376.23632 506.95450 -76.15751 -7396.72187
## 506 507 508 509 510 511
## 435.45348 -5186.97523 1708.29745 -1429.91534 -8289.74451 -710.56376
## 512 513 514 515 516 517
## -2785.95036 -695.37102 1221.27107 -9615.36768 -7859.53215 24209.88107
## 518 519 520 521 522 523
## 9662.15316 5683.21230 -5549.35075 2605.08614 16818.29945 11219.34220
## 524 525 526 527 528 529
## -24434.15805 -5256.80644 -3911.08046 4407.61927 -536.54620 -11280.58936
## 530 531 532 533 534 535
## 4250.50011 13752.20290 -5180.84347 4187.90341 5355.37596 -2006.57136
## 536 537 538 539 540 541
## -4747.85938 -7262.86353 -2263.80475 8166.59919 -56.28857 -8323.85047
## 542 543 544 545 546 547
## 1661.72917 -760.34091 207.27878 -11191.41341 -11186.64956 1948.07115
## 548 549 550 551 552 553
## 6897.95039 -1451.10036 704.79089 -7858.52350 8449.52610 759.10536
## 554 555 556 557 558 559
## -12096.77630 9046.34861 8506.51079 -83.28756 4670.34261 -3772.93826
## 560 561 562 563 564 565
## 13922.97277 21267.18297 -6764.99326 -9948.14833 6547.27489 -19.07311
## 566 567 568 569 570 571
## 3214.95632 -7622.10489 -17520.99097 6513.16246 6264.55493 1713.46958
## 572 573 574 575 576 577
## 2906.70544 1571.90452 -2364.70552 14528.41621 -9883.14679 -6442.32805
## 578 579 580 581 582 583
## 8536.63292 2656.86012 -6752.82899 7326.79027 -4003.97401 -2963.43733
## 584 585 586 587 588 589
## 15526.54695 -14724.27619 8254.43716 -128.73196 -6407.70226 -925.42111
## 590 591 592 593 594 595
## 85.41226 -10818.76713 1666.98424 -7281.24849 2952.65800 8738.76876
## 596 597 598 599 600 601
## -7658.53574 5717.28540 2580.37252 6692.32907 -3374.65270 5977.71834
## 602 603 604 605 606 607
## -8487.80664 2095.55518 1104.30094 2970.26510 1319.28330 219.53282
## 608 609 610 611 612 613
## -5986.66680 7919.66440 -1362.56858 -2745.70062 -3613.66363 -8375.53329
## 614 615 616 617 618 619
## 11837.83025 4777.92351 -9486.13891 11485.98122 5874.91012 -5762.50994
## 620 621 622 623 624 625
## 26192.55964 -13065.21037 -6969.31371 2994.03965 -4328.13321 -10745.13125
## 626 627 628 629 630 631
## 11176.55292 -21788.73761 -2505.50783 8587.54321 11019.48301 -1701.34559
## 632 633 634 635 636 637
## 33141.42495 -6821.12594 5511.68284 5185.28851 -2488.96255 -5550.01323
## 638 639 640 641 642 643
## -2122.34439 -12602.45952 -2375.62368 -2012.84534 -2641.88490 -2973.32598
## 644 645 646 647 648 649
## 1706.24766 4315.20623 16835.67288 18377.30815 660.52571 4571.64242
## 650 651 652 653 654 655
## 10389.19550 19908.49980 464.32260 -28327.33453 -1515.49931 -2453.83114
## 656 657 658 659 660 661
## 1718.82330 -3339.91642 -10755.87396 1560.45818 4118.92562 -1123.41436
## 662 663 664 665 666 667
## 12919.49725 1218.15876 1671.88200 -11833.06048 1267.34264 1073.73233
## 668 669 670 671 672 673
## -5280.53989 -7511.06223 1983.09801 -3800.39588 2592.12625 -3467.37680
## 674 675 676 677 678 679
## -9418.90032 -8371.94336 -3033.01936 116.33359 2784.30612 640.07225
## 680 681 682 683 684 685
## -3904.54336 -1882.14364 -1392.01476 -8317.18988 4585.61494 -2318.79416
## 686 687 688 689 690 691
## -1473.69148 511.34667 10773.37552 9747.38877 10504.60582 -9796.39798
## 692 693 694 695 696 697
## -3663.74276 -3240.55256 5776.96312 -10488.88356 -7995.03827 -8682.08140
## 698 699 700 701 702 703
## -6333.79431 -4793.05269 3030.46675 -4463.62732 -1957.29263 4161.65813
## 704 705 706 707 708 709
## 31036.13695 9427.86115 23356.14735 1596.42904 8247.85650 22852.28517
## 710 711 712 713 714 715
## 6500.74331 -18254.23887 4780.32500 -5482.10975 -137.47337 443.37662
## 716 717 718 719 720 721
## -17302.84202 -5301.03377 3297.79404 -3050.37879 -13015.31800 4242.62165
## 722 723 724 725 726 727
## -5593.31597 705.17389 -3972.09250 -12484.83841 1329.73862 -1905.03921
## 728 729 730 731 732 733
## -9815.66807 17230.53951 1736.85541 -2761.19250 5676.75667 -8668.89394
## 734 735 736 737 738 739
## -761.73168 8100.01499 -15389.69229 -5949.33143 7369.68048 -4823.06485
## 740 741 742 743 744 745
## 122.14502 1788.95340 -1994.66618 -5206.90506 6373.70809 -6313.84431
## 746 747 748 749 750 751
## 22659.81460 7789.25742 -1984.13202 -7325.21749 23379.31454 -4325.69363
## 752 753 754 755 756 757
## 1362.28621 -14455.59969 56074.43721 26898.87384 15082.76481 -10652.49530
## 758 759 760 761 762 763
## 10599.43600 7309.95913 5806.58856 -46390.98789 -16184.88079 948.07585
## 764 765 766 767 768 769
## -2535.39743 -3476.22445 122824.86069 19342.72387 43756.72621 22637.34367
## 770 771 772 773 774 775
## 12131.92051 15901.33219 25781.43446 -98750.58300 -6751.02975 -35830.63243
## 776 777 778 779 780 781
## 1720.73793 -1243.73085 3381.18790 -7427.48270 -1460.60040 -1960.70603
## 782 783 784 785 786 787
## 3408.94047 -7168.16486 -2252.28576 3903.92064 2252.79179 -2769.63020
## 788 789 790 791 792 793
## -4058.45345 1726.82862 2843.14069 -59.50931 -6710.89936 -5792.61309
## 794 795 796 797 798 799
## -1159.44655 -1274.91094 -7828.82959 -2368.39414 -3282.70333 -2680.56729
## 800 801 802 803 804 805
## 10709.17774 2232.28067 7072.36253 2919.87443 -5450.39363 8182.87365
## 806 807 808 809 810 811
## 9874.09400 -10598.35082 -7397.74957 -7509.62687 2998.57007 4188.96640
## 812 813 814 815 816 817
## -2272.55742 -14168.64626 -4119.99072 6249.28790 8230.54546 -9675.50717
## 818 819 820 821 822 823
## -7756.09488 -9344.80915 9742.37458 -1229.32520 -4378.06130 -8454.41771
## 824 825 826 827 828 829
## 7864.49460 7908.04293 4973.15553 -3102.74435 -713.68647 2889.86696
## 830 831 832 833 834 835
## 4667.41932 1624.71321 -6693.49267 2086.35547 -400.14052 2751.13352
## 836 837 838 839 840 841
## 4139.15285 -1136.82234 -9335.99518 5671.96437 -4863.41726 -7581.28345
## 842 843 844 845 846 847
## 3016.57489 -13047.51695 -2826.52889 11620.72558 1308.15420 632.82268
## 848 849
## -663.48212 4510.55312
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17252.38 20099.48 24353.34 24071.31 26424.61 23757.34 24473.63 19705.89
## 10 11 12 13 14 15 16 17
## 19442.46 16785.29 17562.74 14292.23 14343.39 15008.03 16704.55 15024.62
## 18 19 20 21 22 23 24 25
## 16059.26 15432.81 22515.05 21599.09 21078.96 22968.91 22294.92 22947.45
## 26 27 28 29 30 31 32 33
## 24793.35 18721.92 20448.02 28285.11 28342.76 28015.69 25645.17 27047.56
## 34 35 36 37 38 39 40 41
## 30890.49 31238.90 32648.03 30158.71 34136.92 37344.91 34401.11 31212.09
## 42 43 44 45 46 47 48 49
## 30059.89 20639.71 28158.21 30594.62 31683.79 38521.69 38016.29 42678.33
## 50 51 52 53 54 55 56 57
## 46914.47 39612.61 34181.23 29204.08 22348.72 28638.53 25219.27 21516.99
## 58 59 60 61 62 63 64 65
## 25925.21 27184.72 27481.72 27893.53 23764.59 40355.36 42199.26 37445.37
## 66 67 68 69 70 71 72 73
## 41652.18 46567.68 57235.52 55249.35 40493.00 37998.94 41016.75 35317.07
## 74 75 76 77 78 79 80 81
## 30769.45 21473.19 24674.56 20600.40 22686.40 17582.10 19597.46 18824.01
## 82 83 84 85 86 87 88 89
## 17851.77 15920.76 17198.49 20806.43 25224.01 26213.85 26238.81 26855.52
## 90 91 92 93 94 95 96 97
## 30980.47 29811.06 30810.38 28874.88 28074.73 28442.86 28849.43 22427.09
## 98 99 100 101 102 103 104 105
## 25442.02 18472.72 17328.90 15369.68 15669.43 16346.79 20819.28 19902.82
## 106 107 108 109 110 111 112 113
## 23413.80 23203.78 24879.84 27756.51 25254.71 21698.31 21973.72 24619.90
## 114 115 116 117 118 119 120 121
## 35484.04 33676.95 35514.36 38512.58 40468.42 38156.92 32978.12 29319.64
## 122 123 124 125 126 127 128 129
## 31402.39 29682.40 30863.92 38463.41 38105.89 37167.46 34031.10 35812.24
## 130 131 132 133 134 135 136 137
## 41209.92 40650.24 31852.26 33117.01 36306.66 32717.43 31104.77 30189.80
## 138 139 140 141 142 143 144 145
## 26746.96 28161.33 27930.97 25617.35 27641.71 26266.51 19868.88 22899.29
## 146 147 148 149 150 151 152 153
## 20726.07 23703.07 24243.04 25833.37 26015.44 27669.53 28970.22 32002.26
## 154 155 156 157 158 159 160 161
## 27472.61 26735.60 24290.96 30184.81 41672.80 40003.10 37368.15 42388.22
## 162 163 164 165 166 167 168 169
## 43796.73 47212.50 42678.61 37985.99 43411.01 59712.51 61924.55 60338.71
## 170 171 172 173 174 175 176 177
## 57164.54 55563.85 58266.31 57290.45 49582.89 52406.28 56148.74 56184.93
## 178 179 180 181 182 183 184 185
## 63313.47 53816.16 50567.24 41376.30 32916.00 36424.11 46527.15 45983.19
## 186 187 188 189 190 191 192 193
## 51935.69 57680.42 68462.94 73745.57 67440.54 67633.96 74703.97 70380.60
## 194 195 196 197 198 199 200 201
## 65903.62 55147.93 49178.32 50602.56 46197.45 38366.60 44791.06 43018.54
## 202 203 204 205 206 207 208 209
## 42656.52 43134.85 49928.56 58817.39 58447.01 60171.74 61827.46 65618.47
## 210 211 212 213 214 215 216 217
## 75084.58 67167.74 55357.32 49966.38 40963.12 37920.97 41034.50 31055.13
## 218 219 220 221 222 223 224 225
## 48027.88 55348.34 56250.20 79014.60 86509.27 88512.41 96103.41 87054.73
## 226 227 228 229 230 231 232 233
## 81053.43 80635.38 77285.51 76447.04 81183.84 82548.05 76983.76 72196.93
## 234 235 236 237 238 239 240 241
## 77850.31 64498.73 56535.60 48486.61 40098.57 44290.28 46442.92 39893.91
## 242 243 244 245 246 247 248 249
## 33573.77 43855.76 38102.92 42038.44 34302.67 33008.60 36663.71 39487.47
## 250 251 252 253 254 255 256 257
## 30317.37 36255.15 40056.39 45250.92 47969.10 47461.71 57762.16 75258.53
## 258 259 260 261 262 263 264 265
## 75073.60 68384.58 69866.34 66087.31 67533.38 61291.36 50566.49 46594.04
## 266 267 268 269 270 271 272 273
## 46803.44 42910.35 51714.07 47987.32 52133.86 50250.52 54317.69 54613.70
## 274 275 276 277 278 279 280 281
## 60630.84 58265.19 67937.70 61862.78 62072.80 60421.95 66164.55 59895.33
## 282 283 284 285 286 287 288 289
## 56459.35 46012.14 44408.96 61640.40 67170.24 67579.68 64997.39 64084.80
## 290 291 292 293 294 295 296 297
## 68085.58 71995.64 52993.04 43095.54 37106.84 47427.76 50669.44 49783.43
## 298 299 300 301 302 303 304 305
## 73978.68 79923.83 80591.46 85203.71 83402.36 78432.83 81899.51 56799.40
## 306 307 308 309 310 311 312 313
## 53060.68 52740.47 46530.89 43740.45 47343.65 39896.16 38618.30 33180.06
## 314 315 316 317 318 319 320 321
## 36964.46 36145.83 39977.15 37961.66 63746.99 61588.24 63211.76 71209.58
## 322 323 324 325 326 327 328 329
## 73596.82 99077.54 97455.27 73286.35 72084.24 70441.62 62393.63 59515.97
## 330 331 332 333 334 335 336 337
## 29465.42 33152.86 33592.37 35912.22 35253.08 41019.90 42095.47 37343.19
## 338 339 340 341 342 343 344 345
## 36557.64 36684.30 32003.41 38002.21 38665.60 38924.04 39799.82 41585.70
## 346 347 348 349 350 351 352 353
## 43407.64 43159.30 36104.15 26672.24 32016.76 30877.80 30467.86 28084.37
## 354 355 356 357 358 359 360 361
## 32763.13 36517.33 40980.84 39169.88 40430.14 42568.55 49963.63 50514.21
## 362 363 364 365 366 367 368 369
## 50715.87 53178.98 50662.46 50107.01 42752.36 39949.19 36126.24 33904.97
## 370 371 372 373 374 375 376 377
## 29962.84 37250.60 39537.51 47422.90 41394.64 40841.82 39379.15 38911.94
## 378 379 380 381 382 383 384 385
## 29779.77 34377.80 27429.59 35648.79 45981.69 49547.08 47820.48 49812.13
## 386 387 388 389 390 391 392 393
## 56032.19 65517.51 58728.91 53197.20 52925.91 60305.39 60828.76 69497.33
## 394 395 396 397 398 399 400 401
## 58603.12 60169.99 59730.11 59213.97 57700.89 56462.47 43221.20 51806.79
## 402 403 404 405 406 407 408 409
## 50807.21 49773.45 56174.70 48717.22 48028.38 46355.06 42032.32 40863.03
## 410 411 412 413 414 415 416 417
## 38926.91 33021.08 40893.86 43820.19 38492.51 33580.76 48452.42 52297.21
## 418 419 420 421 422 423 424 425
## 56226.49 48695.63 45018.93 43694.91 47281.38 35700.45 35429.66 29688.42
## 426 427 428 429 430 431 432 433
## 35278.29 43605.93 50498.11 47263.54 44332.21 41257.37 41145.56 37623.35
## 434 435 436 437 438 439 440 441
## 33761.71 30995.73 32571.83 34421.23 32425.83 37292.03 43497.61 40249.94
## 442 443 444 445 446 447 448 449
## 39955.93 42962.38 40848.43 44837.99 40084.84 31117.02 29954.48 41311.53
## 450 451 452 453 454 455 456 457
## 40993.07 46644.33 42280.40 42630.07 44250.60 47968.85 37842.98 42692.34
## 458 459 460 461 462 463 464 465
## 38115.52 45684.69 49204.78 51825.38 48554.87 50895.69 51097.23 52844.29
## 466 467 468 469 470 471 472 473
## 52341.89 55285.69 52613.93 57665.20 50925.02 48535.90 47122.03 43746.32
## 474 475 476 477 478 479 480 481
## 47502.51 54965.98 49392.84 51096.10 45884.85 44263.82 47096.82 36510.84
## 482 483 484 485 486 487 488 489
## 30072.28 31941.64 34638.36 36128.36 37094.24 30734.91 43265.38 49925.69
## 490 491 492 493 494 495 496 497
## 56756.10 51467.48 56300.61 63934.42 67745.90 54001.97 44579.67 42604.61
## 498 499 500 501 502 503 504 505
## 42927.82 43718.99 38205.62 40604.59 45906.19 51587.90 52297.59 52408.15
## 506 507 508 509 510 511 512 513
## 46109.98 47449.98 43709.13 46464.63 46130.32 39845.99 40977.09 40152.23
## 514 515 516 517 518 519 520 521
## 41257.87 43897.94 36737.96 32017.26 55907.28 64068.07 67721.07 61100.06
## 522 523 524 525 526 527 528 529
## 62439.56 76025.37 83002.16 57952.09 52822.08 49516.38 53895.40 53401.73
## 530 531 532 533 534 535 536 537
## 43585.21 48577.08 61237.70 55758.53 59156.20 63144.00 60196.57 55227.29
## 538 539 540 541 542 543 544 545
## 48689.52 47345.40 55282.57 55032.99 47592.99 49816.63 49643.29 50337.13
## 546 547 548 549 550 551 552 553
## 40986.08 32821.79 37163.62 45280.24 45077.21 46783.09 40792.90 49805.89
## 554 555 556 557 558 559 560 561
## 50961.20 40740.37 50281.35 58144.14 57509.09 61106.80 56874.03 68634.53
## 562 563 564 565 566 567 568 569
## 85323.14 75414.15 63977.73 68396.93 66521.33 67707.96 59277.99 43267.12
## 570 571 572 573 574 575 576 577
## 50275.73 56180.82 57363.58 59439.10 60086.13 57212.58 69459.15 58832.61
## 578 579 580 581 582 583 584 585
## 52555.65 60157.14 61661.11 54755.21 61021.69 56597.87 53642.45 67212.42
## 586 587 588 589 590 591 592 593
## 52641.13 59985.30 59077.70 52799.99 52105.16 52381.20 43097.16 45893.96
## 594 595 596 597 598 599 600 601
## 40520.48 44766.23 53529.39 46860.71 52719.63 55097.39 60766.37 56924.57
## 602 603 604 605 606 607 608 609
## 61738.24 53307.02 55186.98 55963.31 58271.43 58845.47 58386.24 52563.76
## 610 611 612 613 614 615 616 617
## 59625.28 57685.41 54782.66 51488.82 44451.88 55961.93 59849.28 50784.88
## 618 619 620 621 622 623 624 625
## 61186.66 65371.51 58861.44 81088.50 66211.60 58541.10 60543.99 55897.42
## 626 627 628 629 630 631 632 633
## 46233.02 56940.17 37496.94 37357.17 46925.23 57407.63 55452.29 84180.55
## 634 635 636 637 638 639 640 641
## 74367.03 76567.71 78204.96 72931.44 65650.92 62285.32 50190.62 48558.99
## 642 643 644 645 646 647 648 649
## 47450.60 45932.90 44317.61 46994.37 51611.61 66581.98 81005.76 78129.21
## 650 651 652 653 654 655 656 657
## 79032.95 84904.21 98348.39 93107.19 63378.36 60830.26 57784.75 58769.34
## 658 659 660 661 662 663 664 665
## 55210.45 45623.54 48007.79 52325.41 51517.65 63078.98 62956.69 63246.20
## 666 667 668 669 670 671 672 673
## 51702.09 53061.55 54079.97 49418.92 43398.90 46433.68 44032.59 47519.23
## 674 675 676 677 678 679 680 681
## 45271.76 38109.66 32767.88 32765.38 35514.27 40246.07 42506.40 40511.00
## 682 683 684 685 686 687 688 689
## 40534.59 40983.33 35325.96 41655.08 41152.55 41451.80 43447.20 54154.47
## 690 691 692 693 694 695 696 697
## 62611.39 70660.26 59957.60 55965.55 52848.04 58001.88 48295.18 41994.51
## 698 699 700 701 702 703 704 705
## 35890.51 32609.77 31089.82 36596.20 34859.86 35532.48 41465.15 70123.28
## 706 707 708 709 710 711 712 713
## 76281.57 93827.86 90147.29 92742.43 107766.83 106607.52 83970.53 84317.82
## 714 715 716 717 718 719 720 721
## 75656.62 72759.48 70736.13 53466.75 48865.35 52357.24 49862.18 38977.95
## 722 723 724 725 726 727 728 729
## 44545.60 40817.11 43062.09 40937.41 31645.26 35595.75 36220.95 29856.89
## 730 731 732 733 734 735 736 737
## 47923.43 50170.91 48204.96 53858.47 46265.59 46540.13 54520.98 40973.47
## 738 739 740 741 742 743 744 745
## 37385.75 45886.35 42661.14 44163.62 46932.09 46045.33 42464.72 49452.99
## 746 747 748 749 750 751 752 753
## 44474.47 65435.03 70754.85 66864.50 58800.54 78577.84 71652.71 70572.03
## 754 755 756 757 758 759 760 761
## 55810.56 104526.27 121595.24 126183.78 107711.42 110139.47 109386.98 107416.42
## 762 763 764 765 766 767 768 769
## 60098.74 45151.21 47060.25 45684.94 43661.71 152222.56 156658.99 181860.80
## 770 771 772 773 774 775 776 777
## 185426.94 179365.24 177362.85 184244.30 81472.60 72062.78 38440.98 41873.59
## 778 779 780 781 782 783 784 785
## 42282.53 46679.77 41079.17 41399.13 41241.77 45794.88 40532.71 40230.22
## 786 787 788 789 790 791 792 793
## 45343.64 48368.06 46622.74 43972.31 46710.72 50077.94 50483.76 45028.04
## 794 795 796 797 798 799 800 801
## 41064.45 41649.34 42059.40 36692.54 36774.27 36047.00 35937.68 47538.58
## 802 803 804 805 806 807 808 809
## 50267.49 56879.27 59027.54 53592.41 60753.76 68486.78 57358.46 50433.34
## 810 811 812 813 814 815 816 817
## 44286.29 48095.89 52463.56 50634.50 38645.13 36949.85 44526.88 52876.36
## 818 819 820 821 822 823 824 825
## 44528.38 38912.81 32619.63 43795.61 43974.06 41379.42 35552.08 44716.81
## 826 827 828 829 830 831 832 833
## 52760.56 57223.32 54067.12 53396.99 55959.44 59750.57 60404.35 53709.22
## 834 835 836 837 838 839 840 841
## 55530.28 54949.01 57193.99 60367.54 58531.00 49764.46 55216.56 50776.14
## 842 843 844 845 846 847 848 849
## 44523.14 48318.52 37601.39 37167.99 49409.56 51094.61 51976.62 51614.73
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8095
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.212953 0.7782841 3.929457
## t2* 2717.032474 161.3238557 879.418439
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.152494 5.224245 13.44303
## 2 lag_depvar 1640.751524 2765.829145 4505.72557
Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)
## =-=-=-=-= Iteration 0 Mon Aug 4 01:05:06 2025
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## =-=-=-=-=
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## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 4 01:05:54 2025
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## =-=-=-=-= Iteration 12000 Mon Aug 4 01:06:04 2025
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## =-=-=-=-=
#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 8.205857 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 192.101143 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 54.522571 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 1.884286 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 26.407143 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 14.927000 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 15.711429 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 313.759429 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2777, min uf: 26799.01, min date: 2018-01-01
#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
## Warning: Removed 2777 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
##
## invoke
## The following object is masked from 'package:data.table':
##
## :=
model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-08-09 00:04:58 sería de: 26.443 pesos// Percentil 95% más alto proyectado: 34.867,93
Según TimeGPT: La proyección de la UF a 298 días más 2026-06-03 sería de: 39.627,14 pesos// Percentil 80% más alto proyectado: 39.944,44 pesos// Percentil 95% más alto proyectado: 40.252,42
Según prophet: La proyección de la UF a 298 días más 2026-06-03 sería de: 42.418 pesos// Percentil 95% más alto proyectado: 51.098
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26067.78 | 26331.96 |
| Lo.80 | 26196.95 | 26493.62 |
| Point.Forecast | 26442.72 | 26799.01 |
| Hi.80 | 31244.83 | 32070.87 |
| Hi.95 | 34130.49 | 34861.62 |
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t
Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4788 1028.6175
## s.e. 0.1010 41.4439
##
## sigma^2 = 38077: log likelihood = -521.14
## AIC=1048.28 AICc=1048.61 BIC=1055.35
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4669 735.1797 9.0494
## s.e. 0.1009 315.9266 9.6535
##
## sigma^2 = 38168: log likelihood = -520.71
## AIC=1049.42 AICc=1049.97 BIC=1058.85
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 611.0632 | 592.9478 | 566.8658 |
| Lo.80 | 760.9455 | 743.7341 | 659.1232 |
| Point.Forecast | 1044.0800 | 1028.5762 | 876.3411 |
| Hi.80 | 1327.2145 | 1313.4183 | 1165.1444 |
| Hi.95 | 1477.0969 | 1464.2046 | 1354.7715 |
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.3 DT_0.33 autoplotly_0.1.4
## [13] rvest_1.0.4 plotly_4.11.0 xts_0.14.1
## [16] forecast_8.24.0 wordcloud_2.6 RColorBrewer_1.1-3
## [19] SnowballC_0.7.1 tm_0.7-16 NLP_0.3-2
## [22] tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.0
## [25] dplyr_1.1.4 purrr_1.1.0 tidyr_1.3.1
## [28] tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0
## [31] sjPlot_2.9.0 lattice_0.22-6 gridExtra_2.3
## [34] plotrix_3.8-4 sparklyr_1.9.1 httr_1.4.7
## [37] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [40] stringi_1.8.7 DataExplorer_0.8.4 data.table_1.17.8
## [43] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [46] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.17.1 jsonlite_2.0.0 magrittr_2.0.3
## [4] farver_2.1.2 rmarkdown_2.29 vctrs_0.6.5
## [7] askpass_1.2.1 janitor_2.2.1 htmltools_0.5.8.1
## [10] curl_6.4.0 janeaustenr_1.0.0 cellranger_1.1.0
## [13] Formula_1.2-5 TTR_0.24.4 StanHeaders_2.32.10
## [16] parallelly_1.45.1 sass_0.4.10 KernSmooth_2.23-22
## [19] bslib_0.9.0 htmlwidgets_1.6.4 tokenizers_0.3.0
## [22] extraDistr_1.10.0 httr2_1.2.1 cachem_1.1.0
## [25] uuid_1.2-1 networkD3_0.4.1 igraph_2.1.4
## [28] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.7-0
## [31] R6_2.6.1 fastmap_1.2.0 future_1.67.0
## [34] snakecase_0.11.1 selectr_0.4-2 digest_0.6.37
## [37] colorspace_2.1-1 spatial_7.3-17 crosstalk_1.2.1
## [40] labeling_0.4.3 timechange_0.3.0 abind_1.4-8
## [43] compiler_4.4.0 bit64_4.6.0-1 withr_3.0.2
## [46] inline_0.3.21 tseries_0.10-58 carData_3.0-5
## [49] DBI_1.2.3 QuickJSR_1.8.0 pkgbuild_1.4.8
## [52] gplots_3.2.0 MASS_7.3-60.2 openssl_2.3.3
## [55] rappdirs_0.3.3 loo_2.8.0 fBasics_4041.97
## [58] gtools_3.9.5 caTools_1.18.3 tools_4.4.0
## [61] lmtest_0.9-40 quantmod_0.4.28 future.apply_1.20.0
## [64] nnet_7.3-19 glue_1.8.0 quadprog_1.5-8
## [67] nlme_3.1-164 nixtlar_0.6.2 generics_0.1.4
## [70] gtable_0.3.6 tzdb_0.5.0 hms_1.1.3
## [73] xml2_1.3.8 car_3.1-3 pillar_1.11.0
## [76] vroom_1.6.5 bit_4.6.0 tidyselect_1.2.1
## [79] its.analysis_1.6.0 knitr_1.50 urca_1.3-4
## [82] stats4_4.4.0 xfun_0.52 matrixStats_1.5.0
## [85] timeDate_4041.110 rstan_2.32.7 lazyeval_0.2.2
## [88] yaml_2.3.10 boot_1.3-30 evaluate_1.0.4
## [91] codetools_0.2-20 timeSeries_4041.111 data.tree_1.1.0
## [94] cli_3.6.5 RcppParallel_5.1.10 systemfonts_1.2.3
## [97] jquerylib_0.1.4 globals_0.18.0 dbplyr_2.5.0
## [100] anytime_0.3.12 parallel_4.4.0 ggfortify_0.4.19
## [103] ellipsis_0.3.2 fracdiff_1.5-3 assertthat_0.2.1
## [106] bitops_1.0-9 listenv_0.9.1 viridisLite_0.4.2
## [109] slam_0.1-55 crayon_1.5.3
#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))